Abstract
Multiple coherent reflections from different parts of a radar
target, or multiple reflection paths through a transmission environment can lead to errors
when estimating their directions of arrival (DOA). This problem is particularly prevalent
in radar tracking systems based on monopulse, a technique which compares the outputs from
two conventionally for med beams to measure the target's angular offset from a known
'look' direction. These errors, commonly known as glint, arise because of the incorrect
assumption that a received signal wave front emanating from a far field target will be
planar and will be normal to the target's DOA. The signals from several closely spaced
point reflectors within a target can combine to create singularities in the scattered
field. These are small regions of space where severe destructive interference leads to
signal fading and where local curvature in the wave fronts means that they are no longer
normal to the target direction. Various subspace ideas have been proposed for the general
resolution of multiple coherent signals using phased array receivers. Particularly
relevant are MUSIC when combined with an interpolated version of spatial smoothing, and
the IMP algorithm. Both methods model the target signals by a multi-dimensional subspace
and attempt to resolve individual scatterers. We present a simplification of these ideas,
specifically applicable to radar tracking systems, in which a subspace of very low
dimensionality is used to approximate the signal from a cluster of many closely spaced
scatterers. The concept of 'distance' between the signal subspace and a fixed reference
subspace is used directly to measure the target position without the need for a peak
search or polynomial rooting algorithm. A suitable projection based algorithm is described
which is comparable in complexity to the monopulse method and this is shown to give mean
DOA estimation errors which are robust against glint.